Question Answering
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@@ -1,6 +1,7 @@
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  ---
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  license: apache-2.0
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  datasets:
 
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  - rajpurkar/squad
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  - google-research-datasets/natural_questions
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  - hotpotqa/hotpot_qa
@@ -11,13 +12,13 @@ pipeline_tag: question-answering
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  - Project Type: Bring your own method
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  ## Structure
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- - `data/` contains the data used for the project (after running `load_data.py`, and downloading the natural questions)
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- - `distilbert.py` contains the code for the DistilBERT model and the Dataset. A function for testing the functionality is in there too.
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  - `distilbert.ipynb` contains the creation and training of the DistilBERT model
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  - `distilbert.model` is the distilbert model
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  - `distilbert_reuse.model` is the question answering model
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- - `load_data.py` contains the code for loading the data and preprocessing it. We also split it up into smaller files to load in the Dataset later on.
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- - `qa_model.py` contains the code for thee different QA models. We also define a separate Dataset class in there and a method for testing the models.
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  - `qa_model.ipynb` contains the creation and training of the QA models.
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  - `requirements.txt` contains the requirements for the project
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  - `utils.py` contains some helper functions for the project. It contains the functions to evaluate the models and a way to visualise the trained parameters for each model.
@@ -63,7 +64,7 @@ Now for the Question Answering model.
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  * Target for Error Metric:
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  * EM: 0.6
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  * F-1: 0.7
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- * Achieved value: I almost achieved the target for both of the measurements. I ultimately quit I had already spent a lot of time on the project and thought that the results were reasonable.
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  * EM: 0.52
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  * F-1: 0.67
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  ---
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  license: apache-2.0
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  datasets:
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+ - Skylion007/openwebtext
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  - rajpurkar/squad
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  - google-research-datasets/natural_questions
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  - hotpotqa/hotpot_qa
 
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  - Project Type: Bring your own method
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  ## Structure
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+ - `data/` contains the data used for the project
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+ - `distilbert.py` contains the code for the DistilBERT model and the Dataset.
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  - `distilbert.ipynb` contains the creation and training of the DistilBERT model
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  - `distilbert.model` is the distilbert model
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  - `distilbert_reuse.model` is the question answering model
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+ - `load_data.py` contains the code for loading the data and preprocessing it.
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+ - `qa_model.py` contains the code for thee different QA models.
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  - `qa_model.ipynb` contains the creation and training of the QA models.
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  - `requirements.txt` contains the requirements for the project
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  - `utils.py` contains some helper functions for the project. It contains the functions to evaluate the models and a way to visualise the trained parameters for each model.
 
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  * Target for Error Metric:
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  * EM: 0.6
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  * F-1: 0.7
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+ * Achieved value: I almost achieved the target for both of the measurements.
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  * EM: 0.52
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  * F-1: 0.67
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